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The rise of internet purchasing and the prevalence of e-commerce platforms have given rise to an unprecedented influx of online reviews. These reviews wield substantial influence over consumer decision-making, serving as a barometer for product and service quality. Yet, as the importance of internet reviews has soared, so too has the proliferation of deceptive and counterfeit reviews. These fake reviews, deliberately crafted to mislead potential customers and manipulate ratings, pose a profound challenge to the integrity of online commerce. Consequently, the imperative to establish a robust and dependable system for detecting false reviews cannot be overstated. In the pursuit of this critical objective, our research paper introduces a pioneering fake review detection system, distinguished by its ability to identify and mitigate various manifestations of deceptive reviews. These encompass short text attacks, overlapping text attacks, substantial duplicate review campaigns, and reviews that are incongruent with the product or service they purport to evaluate. Notably, our system achieves an impressive average accuracy of 95%, a milestone realized through the development of a neural network crafted from the ground up. The efficacy of our neural network underscores the potential of cutting-edge machine learning technology in combating the proliferation of fake reviews. Beyond its academic significance, our research holds profound practical implications. By fostering trust in online reviews, this system bolsters consumer confidence, supports businesses in maintaining their reputations, and contributes to the integrity of the e-commerce ecosystem. As online commerce continues its inexorable ascent, our research illuminates a path forward in the perpetual battle against deceptive reviews. Future research endeavors may explore opportunities for further refinement and enhancement of our neural network-based detection system, as well as the continued evolution of strategies for countering emerging threats in the ever-evolving landscape of fake review generation and manipulation.
Sikchi et al. (Tue,) studied this question.